Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning (2505.17315v1)

Abstract: Recent LLMs exhibit strong reasoning capabilities, yet the influence of long-context capacity on reasoning remains underexplored. In this work, we hypothesize that current limitations in reasoning stem, in part, from insufficient long-context capacity, motivated by empirical observations such as (1) higher context window length often leads to stronger reasoning performance, and (2) failed reasoning cases resemble failed long-context cases. To test this hypothesis, we examine whether enhancing a model's long-context ability before Supervised Fine-Tuning (SFT) leads to improved reasoning performance. Specifically, we compared models with identical architectures and fine-tuning data but varying levels of long-context capacity. Our results reveal a consistent trend: models with stronger long-context capacity achieve significantly higher accuracy on reasoning benchmarks after SFT. Notably, these gains persist even on tasks with short input lengths, indicating that long-context training offers generalizable benefits for reasoning performance. These findings suggest that long-context modeling is not just essential for processing lengthy inputs, but also serves as a critical foundation for reasoning. We advocate for treating long-context capacity as a first-class objective in the design of future LLMs.

Longer Context, Deeper Thinking: Examining Long-Context Ability in Reasoning

The paper "Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning" dives into a nuanced exploration of LLMs' reasoning capabilities, specifically focusing on their long-context ability. This paper addresses a critical question in AI research: how does long-context capacity influence reasoning performance?

Study Motivation and Hypothesis

The authors build their hypothesis on empirical observations suggesting that the ability of models to handle longer contexts is a pivotal factor in enhancing reasoning capabilities. They highlight key observations: models with extended context windows show superior accuracy on reasoning benchmarks, failed reasoning outputs often mimic issues seen in long-context scenarios, and reasoning datasets increasingly feature longer input sequences. These insights lay the foundation for the hypothesis that boosting a model's long-context ability prior to Supervised Fine-Tuning (SFT) could enhance its reasoning performance.

Methodology and Experimentation

The researchers employ controlled experiments comparing models with varying long-context capacities but identical architectures and fine-tuning data. They extend context lengths using RoPE theta scaling and model merging techniques, evaluating long-context and reasoning performance across several benchmarks, including MATH500, AIME22–24, and GSM8K. The experiments reveal a consistent trend: models with stronger long-context capacity consistently outperform those with lesser capacity on reasoning tasks post-SFT. Additionally, these improvements persist even for tasks with shorter input lengths, suggesting that long-context training offers cognitive benefits beyond mere sequence processing.

The research further explores whether extremely lengthy context windows (e.g., 128K tokens) contribute additional gains. By employing linear merging strategies with models capable of handling up to 1M tokens, they find that extreme context length indeed bolsters reasoning performance, albeit with diminishing returns if the model's effective long-context ability is not robust.

Key Findings

1. Correlation Between Long-Context Capacity and Reasoning: The paper reveals a clear correlation between enhanced long-context ability and improved reasoning performance across benchmarks. This indicates that long-context modeling is foundational for processing complex reasoning tasks.
2. Effective Recipe for Reasoning Fine-Tuning: The authors advocate enhancing a model's long-context capacity as a preparatory step before reasoning-specific SFT, showing substantial improvements in accuracy and output quality across multiple benchmarks.
3. Incremental Gains with Extreme Context Lengths: While exceptionally long sequences do contribute positively to reasoning performance, the benefits may plateau without effective utilization mechanics within the model.

Implications and Future Directions

The implications are twofold: practically, this research suggests optimizing models for long-context scenarios can be pivotal in enhancing reasoning abilities, making it a priority in model design and training regimes. Theoretically, it underscores the role of context integration in cognitive modeling, bridging current capabilities toward more complex reasoning tasks. Future research could extend these findings to larger models and across diverse applications, further clarifying how long-context abilities can be maximally leveraged in AI systems. Additionally, exploring optimal strategies for adapting long-context modeling for diverse reasoning datasets remains an intriguing avenue for continued exploration.